JSSC 2019第1期RF & Wireless40nm

A 2321996-kSs Robust Compressive Sensing Reconstruction Engine for Real-Time Phy

提出一种232-1996kS/s的鲁棒压缩感知重建引擎，用于实时生理信号监测。

232-1996kS/s, 40nm CMOS

压缩感知鲁棒重建生理信号监测VLSI架构无线传感器网络

▸创新点1：结合稀疏估计（SE）与鲁棒子空间追踪（SP）的方法创新，通过联合优化算法在噪声环境下实现超过8dB的信噪比增益，显著提升生理信号重建的鲁棒性和成功率。

▸创新点2：采用基于梯度下降法的灵活索引更新VLSI架构创新，支持任意信号维度的压缩感知重建，无需矩阵分解，降低了硬件复杂度并提高了计算效率。

▸创新点3：通过并行搜索、索引旁路和处理单元（PEs）分组的系统级创新，将压缩感知重建周期延迟降低约6.3倍，显著提升实时信号处理的吞吐量。

▸创新点4：在40nm CMOS工艺下实现的2321996-kS/s压缩感知重建引擎，通过硬件优化（如PEs分组）和算法协同设计，为基于压缩感知的无线生物传感器提供实时噪声环境下的生理信号重建能力。

Abstract

Compressive sensing (CS) techniques enable new reduced-complexity designs for sensor nodes and help reduce overall transmission power in wireless sensor network. However, for real-time physiological signals monitoring, the orthogonal matching pursuit that applied prior CS reconstruction chip designs is sensitive to measurement noise and suffers from a low convergence rate. In this paper, we present a robust 232–1996-kS/s CS reconstruction engine fabricated in 40-nm CMOS. With combination sparsit